Joseph Powell - Using the transcriptome to determine the genetic mechanisms of disease susceptibillity
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Joseph Powell - Using the transcriptome to determine the genetic mechanisms of disease susceptibillity

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Epistasis is the phenomenon whereby one polymorphism’s effect on a trait depends on other polymorphisms present in the genome. The extent to which epistasis influences complex traits and......

Epistasis is the phenomenon whereby one polymorphism’s effect on a trait depends on other polymorphisms present in the genome. The extent to which epistasis influences complex traits and contributes to their variation is a fundamental question in evolution and human genetics. Although often demonstrated in artificial gene manipulation studies in model organisms, and some examples have been reported in other species, few examples exist for epistasis among natural polymorphisms in human traits. Its absence from empirical findings may simply be due to low incidence in the genetic control of complex traits, but an alternative view is that it has previously been too technically challenging to detect owing to statistical and computational issues. Here we show, using advanced computation and a gene expression study design, that many instances of epistasis are found between common single nucleotide polymorphisms (SNPs). In a cohort of 846 individuals with 7,339 gene expression levels measured in peripheral blood, we found 501 significant pairwise interactions between common SNPs influencing the expression of 238 genes (P < 2.91 × 10−16). Replica􀆟on of these interac􀆟ons in two independent data sets showed both concordance of direction of epistatic effects (P = 5.56 × 10−31) and enrichment of interac􀆟on P values, with 30 being significant at a conservative threshold of P < 9.98 × 10−5. Forty‐four of the genetic interactions are
located within 5 megabases of regions of known physical chromosome interactions (P = 1.8 × 10−10). Epista􀆟c networks of three SNPs or more influence the expression levels of 129 genes, whereby one cis‐acting SNP is modulated by several trans‐acting SNPs. For example, MBNL1 is influenced by an additive effect at rs13069559, which itself is masked by trans‐SNPs on 14 different chromosomes, with nearly identical enotype–phenotype maps for each cis–trans interaction. This study presents the first evidence, to our knowledge, for many instances of segregating common polymorphisms interacting to influence human traits.

First presented at the 2014 Winter School in Mathematical and Computational Biology http://bioinformatics.org.au/ws14/program/

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  • 1. Common Diseases Epistasis Variance Heterogeneity Using the transcriptome to determine the genetic mechanisms of disease susceptibility Joseph Powell Centre for Neurogenetics and Systems Genomics Winter School in Mathematical and Computational Biology Brisbane - 7th July 2014 Joseph Powell Mechanisms of disease susceptibility
  • 2. Common Diseases Epistasis Variance Heterogeneity Causes of Death - Common Diseases Joseph Powell Mechanisms of disease susceptibility
  • 3. Common Diseases Epistasis Variance Heterogeneity Causes of Common Diseases - Genetics + Environment Environmental factors Genetics Joseph Powell Mechanisms of disease susceptibility
  • 4. Common Diseases Epistasis Variance Heterogeneity How Do Mutations Influence Disease Susceptibility? Most genetic variation underlying disease susceptibility is located in regulatory regions Joseph Powell Mechanisms of disease susceptibility
  • 5. Common Diseases Epistasis Variance Heterogeneity Move To Molecular Phenotypes - Systems Genetics Joseph Powell Mechanisms of disease susceptibility
  • 6. Common Diseases Epistasis Variance Heterogeneity By what means do most DNA variants alter cellular behaviour and ultimately contribute to differences in disease susceptibility? Joseph Powell Mechanisms of disease susceptibility
  • 7. Common Diseases Epistasis Variance Heterogeneity Genome-wide epistasis Variance heterogeneity Joseph Powell Mechanisms of disease susceptibility
  • 8. Common Diseases Epistasis Variance Heterogeneity What is epistasis? Definition The effect on the phenotype caused by locus A depends on the genotype at locus B.... Epistasis has been reported in model organisms through artificial gene knockouts, artificial line crosses and hybridization. Our aim was to systematically search for instances of epistasis amongst common variants for genetic variation that has arisen in natural populations. Joseph Powell Mechanisms of disease susceptibility
  • 9. Common Diseases Epistasis Variance Heterogeneity Gene expression Transcription measured for thousands of genes Typically heritable Loci (eQTLs) commonly have very large effect sizes Good candidates to search for epistasis Joseph Powell Mechanisms of disease susceptibility
  • 10. Common Diseases Epistasis Variance Heterogeneity Joseph Powell Mechanisms of disease susceptibility
  • 11. Common Diseases Epistasis Variance Heterogeneity Discovery population 846 healthy individuals 528,509 autosomal SNPs RNA measured for peripheral blood (Illumina HT-12v4.0) Expression levels for 7,339 probes Joseph Powell Mechanisms of disease susceptibility
  • 12. Common Diseases Epistasis Variance Heterogeneity Joseph Powell Mechanisms of disease susceptibility
  • 13. Common Diseases Epistasis Variance Heterogeneity Computational analysis Exhaustive SNP x SNP testing for each probe epiGPU software and GPU clusters 8 d.f. F-test Over quadrillion tests Joseph Powell Mechanisms of disease susceptibility
  • 14. Common Diseases Epistasis Variance Heterogeneity Design Of The Analysis SNP x SNP 2 dimension analysis Epistatic genotype by phenotype Example of genotype by phenotype epistasis map AA Aa aa aa Aa AA 0.0 0.2 0.4 0.6 0.8 1.0 SNP1 SNP2 RNA levels Joseph Powell Mechanisms of disease susceptibility
  • 15. Common Diseases Epistasis Variance Heterogeneity Joseph Powell Mechanisms of disease susceptibility
  • 16. Common Diseases Epistasis Variance Heterogeneity Correction for multiple testing Apply  a  family-­‐wise   error  rate  of  5%   Permutation and Bonferroni used to give 5% FWER Permutation: Single probe FWER; T∗ = 2.13x10−12 Correct for 7,339 probes Experiment wide FWER; T ∗ /7, 339 = 2.91x10−16 Joseph Powell Mechanisms of disease susceptibility
  • 17. Common Diseases Epistasis Variance Heterogeneity Joseph Powell Mechanisms of disease susceptibility
  • 18. Common Diseases Epistasis Variance Heterogeneity Removing artifacts For SNP pairs that passed the 2.91x10−16 threshold All 9 genotypes classes present Minimum class size of 5 No LD between SNP pairs (r2 < 0.1 and D 2 < 0.1) No single loci additive or dominance effects 11,155 pairs carried forward Joseph Powell Mechanisms of disease susceptibility
  • 19. Common Diseases Epistasis Variance Heterogeneity Joseph Powell Mechanisms of disease susceptibility
  • 20. Common Diseases Epistasis Variance Heterogeneity Testing for non-additive effects Nested ANOVA for 11,155 pairs Contrasts full genetic (8 d.f.) vs marginal effects (4 d.f) Thus, testing for the contribution of epistatic variance Joseph Powell Mechanisms of disease susceptibility
  • 21. Common Diseases Epistasis Variance Heterogeneity Joseph Powell Mechanisms of disease susceptibility
  • 22. Common Diseases Epistasis Variance Heterogeneity More correction for multiple testing Epistatic effects significant at p < 0.05/11, 155 = 4.48x10−6 501 SNP pairs with significant interaction terms Joseph Powell Mechanisms of disease susceptibility
  • 23. Common Diseases Epistasis Variance Heterogeneity Joseph Powell Mechanisms of disease susceptibility
  • 24. Common Diseases Epistasis Variance Heterogeneity Replication population Fehrmann 1,240 individuals (Netherlands) EGCUT 891 individuals (Estonian) RNA measured for peripheral blood (Illumina HT-12v3.0) Genotyped with Illumina arrays Joseph Powell Mechanisms of disease susceptibility
  • 25. Common Diseases Epistasis Variance Heterogeneity Joseph Powell Mechanisms of disease susceptibility
  • 26. Common Diseases Epistasis Variance Heterogeneity Replication tests Replication of 501 epistatic hits using two independent cohorts Same filtering criteria 434 pairs which could be tested in both cohorts Joseph Powell Mechanisms of disease susceptibility
  • 27. Common Diseases Epistasis Variance Heterogeneity Joseph Powell Mechanisms of disease susceptibility
  • 28. Common Diseases Epistasis Variance Heterogeneity Functional work Analyses to elucidate possible functional mechanisms Non-synonymous mutations GWAS and known eQTL overlap Genome segmentation Chromosome interactions Tissue specific transcription regions Joseph Powell Mechanisms of disease susceptibility
  • 29. Common Diseases Epistasis Variance Heterogeneity Q-Q plots of interaction p-values from replication The top panel shows all 434 discovery SNPs that were tested for interactions. The bottom panel shows the same data as the top panel but excluding the 30 most significant interactions Matched direction of epistatic effects p = 5.56x10−31 q q qq q qq q qq q q q q q qq qq qq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq q q q q q q qqq q qq qq q q qqq qqqqqqqq q qq qqq q qqqqqq q qqqqqqqqqqq qqqqqqq qqqq qqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq 0 50 100 150 0 1 2 3 4 AllBelowBonferronithreshold 0 1 2 3 Expected Observed Joseph Powell Mechanisms of disease susceptibility
  • 30. Common Diseases Epistasis Variance Heterogeneity Interactive Figure: http://kn3in.github.io/detecting_epi/ Yellow dots: A Transcript Grey dots: Epistatic SNPs q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q Joseph Powell Mechanisms of disease susceptibility
  • 31. Common Diseases Epistasis Variance Heterogeneity Example: Epistatic Control Of CAST Regulation CAST: Associated with numerous rheumatic diseases It’s regulation controlled by many epistatic SNPs throughout the genome Novel genetic control of regulation potentially underlying disease susceptibility Chromosomes Position of CAST GeneEpistatic interactions Joseph Powell Mechanisms of disease susceptibility
  • 32. Common Diseases Epistasis Variance Heterogeneity Functional Mechanisms - Chromosomal Interactions Chromosome interactions identified in K562 cell lines using Hi-C and ChIP-seq Red lines = N SNPs within 20kb, 500kb, 2Mb and 10Mb of known interacting regions Histograms = null distributions 0 250 500 750 0 250 500 750 0 250 500 750 0 250 500 750 5Mb1Mb250kb10kb 0 10 20 30 40 Number of chromosome interactionsFrequency Joseph Powell Mechanisms of disease susceptibility
  • 33. Common Diseases Epistasis Variance Heterogeneity Conclusions The first evidence for multiple instances of segregating common polymorphisms interacting to influence human traits Novel computational and statistical frameworks can identify and replicate epistasis New knowledge of the control of transcription of many genes implicated in common disease Joseph Powell Mechanisms of disease susceptibility
  • 34. Common Diseases Epistasis Variance Heterogeneity Variance Heterogeneity Searching for veQTL There is evidence across several species for genetic control of phenotypic variation of complex traits This means that the variance of a phenotype is genotype dependent Understanding genetic control of variability is important in evolutionary biology, and medicine Joseph Powell Mechanisms of disease susceptibility
  • 35. Common Diseases Epistasis Variance Heterogeneity Variance eQTL What causes them? Epistatic interactions Gene by Environment Interactions Other ..... ? Joseph Powell Mechanisms of disease susceptibility
  • 36. Common Diseases Epistasis Variance Heterogeneity 846 healthy individuals 6,011,184 Imputed (autosomal) SNPs RNA measured for peripheral blood (Illumina HT-12v4.0) Expression levels for 17,994 probes Joseph Powell Mechanisms of disease susceptibility
  • 37. Common Diseases Epistasis Variance Heterogeneity Double Generalized Linear Model y = µ + xb + e e ∼ N(0, σ2 e ) Model to detect variance and mean effects e ∼ N(0, σ2 ei ); log(σ2 e ) = c + xv y ∼ N(µ + xb, exp(c + vx)) Joseph Powell Mechanisms of disease susceptibility
  • 38. Common Diseases Epistasis Variance Heterogeneity For statistical analysis, the informational content is the same if data is transformed by a monotonic function Provided the three means are different, there is always a monotonic transformation that will tend to equalize the three variances However, it’s relevance depends on the biological scale Joseph Powell Mechanisms of disease susceptibility
  • 39. Common Diseases Epistasis Variance Heterogeneity Original and normalised scales Joseph Powell Mechanisms of disease susceptibility
  • 40. Common Diseases Epistasis Variance Heterogeneity Scale Initial scale = Z-score from inverse-normal transformation Adjust original scale using transformation functions provided in Sun et al. (2013) σ 2 (x) = f (m) = am 2 + bm + c (1) Express variance of x as a function of its mean mx , f (mx ) y = g(x)∞ dx f (x) (2) Re-test using dglm The aim is to remove main driven variance effects Pictorial representation of four different non-linear changes to the scale. They represent four classes of monotonic transformation (Sun et al AJHG 2013). Joseph Powell Mechanisms of disease susceptibility
  • 41. Common Diseases Epistasis Variance Heterogeneity Discovery results Cis - 2.55x10−7 747 veQTL (Potentially have some main effects) Only 88 have Pmain > 0.01 Transformations remove the effects of 655/659 (with main effects) Transformations remove the effects of 1/87 (without main effects) Trans - 3.39x10−12 1412 veQTL (Potentially have some main effects) 619 have Pmain > 0.01 Transformations remove the effects of 732/793 (with main effects) Transformations remove the effects of 21/619 (without main effects) Joseph Powell Mechanisms of disease susceptibility
  • 42. Common Diseases Epistasis Variance Heterogeneity Fehrmann 1,240 individuals (Netherlands) EGCUT 891 individuals (Estonian) RNA measured for peripheral blood (Illumina HT-12v3.0) Genotyped with Illumina arrays Joseph Powell Mechanisms of disease susceptibility
  • 43. Common Diseases Epistasis Variance Heterogeneity Replication of 7,768 veQTL hits using two independent cohorts DGLM Also Bartlett’s and Levene’s tests Joseph Powell Mechanisms of disease susceptibility
  • 44. Common Diseases Epistasis Variance Heterogeneity Replication Meta-analysis Just interested in the variance only veQTL At FDR = 0.05 89 % of cis-veQTL 78 % of trans-veQTL Same effect direction; 85 % cis and 77 % trans Joseph Powell Mechanisms of disease susceptibility
  • 45. Common Diseases Epistasis Variance Heterogeneity Ripple effect across a network q q q q q q q q −5.0 −2.5 0.0 2.5 0 1 2 factor(rs3821224) ILMN_1798308 Predicted TF binding sites tagged by expression probes. Blue lines have variance heterogeneity with rs3821224 (p < 0.05) Expression levels for a probe in AHSA2 by genotype class of rs3821224 Joseph Powell Mechanisms of disease susceptibility
  • 46. Common Diseases Epistasis Variance Heterogeneity GWAS - veSNP overlap Disease / Trait N veSNPs in GWAS catalogue Asthma 1 Autism spectrum disorder 2 Bipolar disorder 2 Cholesterol, total 3 Endometriosis 1 HDL cholesterol 2 Height 4 Inflammatory bowel disease 2 LDL cholesterol 6 N-glycan levels 4 Obesity-related traits 5 Parkinson’s disease 6 Rheumatoid arthritis 3 Schizophrenia 8 Sudden cardiac arrest 2 Systemic lupus erythematosus 3 Triglycerides 7 Type 1 diabetes 3 Urate levels 2 Joseph Powell Mechanisms of disease susceptibility
  • 47. Common Diseases Epistasis Variance Heterogeneity Conclusions Computational and statistical frameworks can identify and replicate variance heterogeneity for RNA transcripts. Variance (only) eQTL represent another form of genetic control for phenotypes. Functional characterization of variance loci suggests a number of possible mechanisms that underlie variance heterogeneity. Joseph Powell Mechanisms of disease susceptibility
  • 48. Common Diseases Epistasis Variance Heterogeneity Acknowledgements Centre for Neurogenetics and Systems Genomics Gib Hemani Kostya Shakhbazov Jian Yang Allan Mcrae Peter Visscher University of Groningen Harm-Jan Westra Lude Franke Dalama University Lars Ronnegard QIMR Berghofer Nick Martin Anjali Henders Grant Montgomery Georgia Institute of Technology Greg Gibson University of Tartu Andres Metspalu Tonu Esko Visit us at www.complextraitgenomics.com Joseph Powell Mechanisms of disease susceptibility